protein-design

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

Protein Graph Library

List of papers about Proteins Design using Deep Learning

Jupyter Notebooks for learning the PyRosetta platform for biomolecular structure prediction and design

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

Fitness landscape exploration sandbox for biological sequence design.

RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

Implementation of trRosetta and trDesign for Pytorch, made into a convenient package, for protein structure prediction and design

Implementation of Chroma, generative models of protein using DDPM and GNNs, in Pytorch

Graph neural network for generating novel amino acid sequences that fold into proteins with predetermined topologies.

Preforms De novo protein design using machine learning and PyRosetta to generate a novel protein structure

A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design

Code to reproduce experiments in "Accelerating Bayesian Optimization for Protein Design with Denoising Autoencoders" (Stanton et al 2022)

Guided Conditional Wasserstein GAN for De Novo Protein Design

Deep Critical Learning. Implementation of ProSelfLC, IMAE, DM, etc.

RosettaDesign using PyRosetta

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

Rosetta FunFolDes – a general framework for the computational design of functional proteins.

SLIP is a sandbox environment for engineering protein sequences with synthetic fitness functions.

Codebase for our preprint using trRosetta to design proteins with discontinuous functional sites, found here: https://www.biorxiv.org/content/10.1101/2020.11.29.402743v1.abstract